It represents the entire population or data sample, and it can be further divided into different sets.ĥ. Root Node: The root node is always the top node of a decision tree. It measures how often a randomly chosen variable would be incorrectly identified.Ĥ. Gini Index: The Gini Index is used to determine the correct variable for splitting nodes. IG( Y, X) = Entropy (Y) - Entropy ( Y | X)ģ. Information Gain: The information gain measures the decrease in entropy after the data set is split. It is calculated using the following formula:Ģ. Entropy handles how a decision tree splits the data. Entropy: Entropy is the measure of uncertainty or randomness in a data set.
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